Improving safety in mixed traffic: A learning-based model predictive control for autonomous and human-driven vehicle platooning

TL;DR

This paper presents a learning-based model predictive control strategy that combines physics-based models with Gaussian process machine learning to enhance safety and prediction accuracy in mixed traffic platoons involving autonomous and human-driven vehicles.

Contribution

It introduces a novel GP-MPC control approach that integrates a Gaussian process model for better HV behavior prediction, improving safety and efficiency in mixed traffic scenarios.

Findings

35.64% reduction in HV speed prediction error

GP-MPC outperforms baseline MPC in safety and efficiency

Enhanced safety during emergency braking scenarios

Abstract

As autonomous vehicles (AVs) become more common on public roads, their interaction with human-driven vehicles (HVs) in mixed traffic is inevitable. This requires new control strategies for AVs to handle the unpredictable nature of HVs. This study focused on safe control in mixed-vehicle platoons consisting of both AVs and HVs, particularly during longitudinal car-following scenarios. We introduce a novel model that combines a conventional first-principles model with a Gaussian process (GP) machine learning-based model to better predict HV behavior. Our results showed a significant improvement in predicting HV speed, with a 35.64% reduction in the root mean square error compared with the use of the first-principles model alone. We developed a new control strategy called GP-MPC, which uses the proposed HV model for safer distance management between vehicles in the mixed platoon. The GP-MPC strategy effectively utilizes the capacity of the GP model to assess uncertainties, thereby significantly enhancing safety in challenging traffic scenarios, such as emergency braking scenarios. In simulations, the GP-MPC strategy outperformed the baseline MPC method, offering better safety and more efficient vehicle movement in mixed traffic.